If you’ve been browsing the internet lately, you’ve undoubtedly heard of machine learning being used in just about everything. You may frequently hear that we live in the age of information and may have even heard of ‘Big Data’. But what does all this mean and what are the ramifications for the world as we see it?

Essentially, this buzz term which shapes the modern corporate landscape refers to the fact that companies have an excess of data; so much so, that these companies are at odds with how they can interpret data and don’t know what to do with it. Data is a pretty powerful thing, which has a lot of untapped potential. Some businesses just sit on that data and do a few rudimentary statistical analyses to make internal business decisions.

But think of the possibilities if we were able to exploit this data. Imagine if a company was able to search through anything consumers had ever publically stated about their product on the internet. This potential use of big data has tremendous corollaries for the marketing and advertising sector as a whole.  This process is known as sentiment analysis, where a machine tries to deduce how humans feel about something through their writing. For a company, this is also invaluable market research, which can be put back into their products/service, to better serve key demographics by improving products based on reviews. Financial traders also sometimes use sentiment analysis to detect fluctuations in stock prices by using the changes in sentiment as indicators of stock value. Sometimes sentiment well before a stock does, which might be profitable.

However, why does this matter to the average person? You might think that only large corporations, multi-million dollar hedge funds and traders benefit from this application of machine learning. But, in fact, this is also to the average person’s advantage.

Perhaps the only limiting factor to this data-fuelled future is the type of information companies have at their disposal. People don’t like having their private information explicitly taken. There are also legal issues about acquiring information from users, related to privacy. Questions such as to what extent companies can take personal information and use it for their benefit rise. Where do privacy and the right to autonomy step in to ensure that we don’t have a Big Brother-esque situation meet?

For example, if McDonald’s decided to predict the next drive through order, by recording data such as your number plate with your Big Mac order, you can bet there would be hues and cries about privacy and right to personal information. Even more problematic, is the potential for identity crimes as possible extensions of this technology can recognise and store facial recognition data to make decisions about you.

It is clear that corporations are staying well away from the use of intimate information, at least at the current time, and only use publicly accessible information to profile you. This sort of thing is constantly happening, evident through the levels of Big Data which we have in this age. You may have already noticed this happening on your Facebook news feed, specifically showing ads, which are creepily specific to what you’ve recently been speaking about or searching. The bigger your data, the more you can learn about anyone.  This is the world that dystopic science fiction writers feared and the adoption of machine learning, albeit less morbid, poses interesting questions for the future.

A more realistic future lies in this carefully thought out balance.

There’s one main suggestion I would make to students in the age of machine learning: learn to code. This trend will continue and become increasingly relevant in all facets of society, particularly in marketing and business operations. The possibilities of data analytics are endless and particularly exciting to keep track of.